Efficient Multi-Objective Optimization for Deep Learning

• URL: http://arxiv.org/abs/2103.13392v1
• Date: Wed, 24 Mar 2021 17:59:42 GMT
• Title: Efficient Multi-Objective Optimization for Deep Learning
• Authors: Michael Ruchte and Josif Grabocka
• Abstract summary: Multi-objective optimization (MOO) is a prevalent challenge for Deep Learning. There exists no scalable MOO solution for truly deep neural networks.
• Score: 2.0305676256390934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-objective optimization (MOO) is a prevalent challenge for Deep Learning, however, there exists no scalable MOO solution for truly deep neural networks. Prior work either demand optimizing a new network for every point on the Pareto front, or induce a large overhead to the number of trainable parameters by using hyper-networks conditioned on modifiable preferences. In this paper, we propose to condition the network directly on these preferences by augmenting them to the feature space. Furthermore, we ensure a well-spread Pareto front by penalizing the solutions to maintain a small angle to the preference vector. In a series of experiments, we demonstrate that our Pareto fronts achieve state-of-the-art quality despite being computed significantly faster. Furthermore, we showcase the scalability as our method approximates the full Pareto front on the CelebA dataset with an EfficientNet network at a tiny training time overhead of 7% compared to a simple single-objective optimization. We make our code publicly available at https://github.com/ruchtem/cosmos.

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